KOSKA Company Overview

KOSKA Corporation has recently published a white paper that enables immediate improvement of profits in small-lot, multi-variety production within the company. This white paper introduces how sales and management can utilize cost management, taking into account the characteristics of small-lot, multi-variety production and responses to the COVID-19 pandemic, to enhance sales and profits. Additionally, it provides a three-step guide that can be immediately implemented to improve the accuracy of estimates specifically for sales activities in small-lot, multi-variety production. 【Table of Contents】 - Differences between mass production and small-lot, multi-variety production - General cost management - Characteristics of estimates and improvements in mass production versus small-lot, multi-variety production - Cost management methods suitable for small-lot, multi-variety production - Cost management during the COVID-19 pandemic - KPI management to improve estimate accuracy - Three steps to manage estimate accuracy 1. Datafication of discrepancies between estimates and actuals for each project 2. Monthly indicatorization of the state of discrepancies between estimates and actuals 3. Discussion of the causes of discrepancies using data - Reasons for not being able to achieve ideal cost management - Barriers to cost management in small-lot, multi-variety production - Cost management service GenKan addresses these challenges - What is GenKan?

◯ Recommended for those who: - Produce a variety of products, but existing visualization tools are mostly geared towards mass production and cannot be used in-house. - Are collecting data from the field in various ways, but are unable to utilize it due to its diverse nature. - Want to achieve visualization that directly leads to improvement activities without the hassle and time of implementation and operation. ◯ Seminar Overview In this webinar, we will introduce methods to achieve visualization of the field that leads to profit improvement in small-batch, multi-product production. Specifically, we will discuss the differences in the visualization data required for mass production versus small-batch, multi-product production, as well as solutions to the challenges of aggregating data from numerous products and the variability in accuracy. *For more details, please refer to the registration page. ■ Event Details Date and Time: February 10, 2020 (Wednesday) 13:00 - 14:00 Participation Fee: Free Location: Online (Zoom) Organizer: KOSKA Co., Ltd. Speaker: Kenichiro Sone (Representative Director, KOSKA Co., Ltd.)

KOSKA Corporation will exhibit at the "Smart Factory EXPO 2021" scheduled to be held at Tokyo Big Sight, as well as at "FOODEX JAPAN 2021" for the food processing industry, scheduled to be held at Makuhari Messe. At these exhibitions, we will provide opportunities to experience our products in a non-face-to-face and non-contact manner as a response to the prevention of the novel coronavirus infection. Specifically, for participants who register by the day of the event, we will send a set of "GenKan" sensors by mail and conduct remote demonstrations that can be carried out in meeting rooms completely free of charge, including shipping costs. ■Situation where participation in the exhibition is not possible Due to the impact of the COVID-19 pandemic, manufacturing companies that have traditionally gathered information by physically interacting with exhibits at trade shows are shifting to participate in online exhibitions. However, relying solely on online information is insufficient, and the time taken from information gathering to purchasing decisions has increased compared to before. For companies actively considering the utilization of IoT sensors and visualization tools, it is crucial to determine whether the sensors can accommodate the characteristics of each process in their own factories and whether they are suitable for the flow of production lines. This can only be accurately assessed by physically interacting with the sensors and confirming specific technical characteristics and operational methods.